作者:Song Li, Tianhe Xu , Nan Jiang, Honglei Yang, Shuaimin Wang, Zhen Zhang来源出版物:Remote Sensing卷:13期:5文献号:DOI:https://doi.org/10.3390/rs13051004出版年:Jan2021

摘要:The meteorological reanalysis data has been widely applied to derive zenith tropospheric delay (ZTD) with a high spatial and temporal resolution. With the rapid development of artificial intelligence, machine learning also begins as a high-efficiency tool to be employed in modeling and predictingZTD. In this paper, we develop three new regionalZTDmodels based on the least squares support vector machine (LSSVM), using both the International GNSS Service (IGS)-ZTDproducts and European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) data over Europe throughout 2018. Among them, theERA5data is extended toERA5S-ZTDandERA5P-ZTDas the background data by the model method and integral method, respectively. Depending on different background data, three schemes are designed to constructZTDmodels based on the LSSVM algorithm, including the without background data, with theERA5S-ZTD, and with theERA5P-ZTD. To investigate the advantage and feasibility of the proposedZTDmodels, we evaluate the accuracy of two background data and three schemes by segmental comparison with theIGS-ZTDof 85IGSstations in Europe. The results show that the overall average Root Mean Square Errors (RMSE) value of all sites is 30.1 mm for theERA5S-ZTD, and 10.7 mm for theERA5P-ZTD. The overall averageRMSEis 25.8 mm, 22.9 mm, and 9 mm for the three schemes, respectively. Moreover, the overall improvement rate is 19.1% and 1.6% for theZTDmodel withERA5S-ZTDandERA5P-ZTD, respectively. In order to explore the reason of the lower improvement for theZTDmodel withERA5P-ZTD, the loop verification is performed by estimating theZTDvalues of each availableIGSstation. In actuality, the monthly improvement rate of estimatedZTDis positive for most stations, and the biggest improvement rate can even reach about 40%. The negative rate mainly comes from specific stations, these stations are located on the edge of the region, near the coast, as well as the lower similarity between the individual verified station and training stations.

Keywords:zenith tropospheric delay (ZTD);least squares support vector machine (LSSVM);European Center for Medium-Range Weather Forecasts Reanalysis 5 (ERA5);Global Navigation Satellite System (GNSS)

Citations:Li, S.; Xu, T.; Jiang, N.; Yang, H.; Wang, S.; Zhang, Z. Regional Zenith Tropospheric Delay Modeling Based on Least Squares Support Vector Machine Using GNSS andERA5Data.Remote Sens.2021,13, 1004. https://doi.org/10.3390/rs13051004